"""simple docstring"""


import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow






@require_torch
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										lowerCamelCase__   ="ylacombe/bark-small"
										lowerCamelCase__   =tempfile.mkdtemp()
										lowerCamelCase__   ="en_speaker_1"
										lowerCamelCase__   ="This is a test string"
										lowerCamelCase__   ="speaker_embeddings_path.json"
										lowerCamelCase__   ="speaker_embeddings"

				def 						_a      ( self      ,					**_lowerCamelCase ):
										return AutoTokenizer.from_pretrained(self.checkpoint      ,					**_lowerCamelCase )

				def 						_a      ( self ):
										shutil.rmtree(self.tmpdirname )

				def 						_a      ( self ):
										lowerCamelCase__   =self.get_tokenizer()

										lowerCamelCase__   =BarkProcessor(tokenizer=_lowerCamelCase )

										processor.save_pretrained(self.tmpdirname )
										lowerCamelCase__   =BarkProcessor.from_pretrained(self.tmpdirname )

										self.assertEqual(processor.tokenizer.get_vocab()      ,					tokenizer.get_vocab() )

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint      ,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,					)
										processor.save_pretrained(
										    self.tmpdirname      ,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,					speaker_embeddings_directory=self.speaker_embeddings_directory      ,					)

										lowerCamelCase__   =self.get_tokenizer(bos_token="(BOS)"      ,					eos_token="(EOS)" )

										lowerCamelCase__   =BarkProcessor.from_pretrained(
										    self.tmpdirname      ,					self.speaker_embeddings_dict_path      ,					bos_token="(BOS)"      ,					eos_token="(EOS)"      ,					)

										self.assertEqual(processor.tokenizer.get_vocab()      ,					tokenizer_add_kwargs.get_vocab() )

				def 						_a      ( self ):
										lowerCamelCase__   =BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint      ,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,					)

										lowerCamelCase__   =35
										lowerCamelCase__   =2
										lowerCamelCase__   =8

										lowerCamelCase__   ={
										    "semantic_prompt": np.ones(_lowerCamelCase ),
										    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
										    "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
										}

										# test providing already loaded voice_preset
										lowerCamelCase__   =processor(text=self.input_string      ,					voice_preset=_lowerCamelCase )

										lowerCamelCase__   =inputs["history_prompt"]
										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()      ,					processed_voice_preset.get(_lowerCamelCase      ,					np.array([] ) ).tolist() )

										# test loading voice preset from npz file
										lowerCamelCase__   =os.path.join(self.tmpdirname      ,					"file.npz" )
										np.savez(_lowerCamelCase      ,					**_lowerCamelCase )
										lowerCamelCase__   =processor(text=self.input_string      ,					voice_preset=_lowerCamelCase )
										lowerCamelCase__   =inputs["history_prompt"]

										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()      ,					processed_voice_preset.get(_lowerCamelCase      ,					np.array([] ) ).tolist() )

										# test loading voice preset from the hub
										lowerCamelCase__   =processor(text=self.input_string      ,					voice_preset=self.voice_preset )






				def 						_a      ( self ):
										lowerCamelCase__   =self.get_tokenizer()

										lowerCamelCase__   =BarkProcessor(tokenizer=_lowerCamelCase )

										lowerCamelCase__   =processor(text=self.input_string )

										lowerCamelCase__   =tokenizer(
										    self.input_string      ,					padding="max_length"      ,					max_length=256      ,					add_special_tokens=_lowerCamelCase      ,					return_attention_mask=_lowerCamelCase      ,					return_token_type_ids=_lowerCamelCase      ,					)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]      ,					encoded_processor[key].squeeze().tolist() )



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   "list[int]":




						'''simple docstring'''



						if upper_limit < 0:
												raise ValueError("Limit for the Catalan sequence must be ≥ 0"							)

						lowerCamelCase__   =[0] * (upper_limit + 1)

						# Base case: C(0) = C(1) = 1
						lowerCamelCase__   =1
						if upper_limit > 0:
												lowerCamelCase__   =1

						# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
						for i in range(2  ,       upper_limit + 1							):
												for j in range(__lowerCAmelCase							):
																		catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

						return catalan_list


if __name__ == "__main__":
	print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
	print('\n*** Enter -1 at any time to quit ***')
	print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
	try:
		while True:
			a				=int(input().strip())
			if N < 0:
				print('\n********* Goodbye!! ************')
				break
			else:
				print(F'''The Catalan numbers from 0 through {N} are:''')
				print(catalan_numbers(N))
				print('Try another upper limit for the sequence: ', end='')
	except (NameError, ValueError):
		print('\n********* Invalid input, goodbye! ************\n')

	import doctest

	doctest.testmod()


